Q1]: Number of Attacks per year
q1_plot = ggplot(q1, aes(x=iyear, y=yearwise_attacks)) +
geom_area(stat = "Identity",color = "darkorange3",size=0.5,fill="blue4")+
geom_point(color="grey50",size=1.5)+labs(title = "No. of Attacks per Year", x= "Year" ,
y="Year Wise Attacks")
ggplotly(q1_plot)
Q2]: Number of bombing per year
q2_plot = ggplot(q2, aes(x=iyear, y=yearwise_bombing)) +
geom_area(stat = "Identity",color = "darkorange3",size=0.5,fill="chocolate2")+
geom_point(color="grey50",size=1.5)+labs(title = "No. of Bombing per Year", x= "Year" ,
y="Year Wise Bombings")
ggplotly(q2_plot)
# Comparison between q1,q2
qn = merge(q1,q2, by = "iyear", all = T)
qn_plot = ggplot(qn, aes(iyear))+geom_line(aes(y=yearwise_attacks, col="No Of Attacks"),size=0.8)+geom_line(aes(y=yearwise_bombing, col="No Of Bombings"),size=0.8)+
geom_point(aes(y=yearwise_attacks),col="blue",size=0.8)+
geom_point(aes(y=yearwise_bombing),col="blue",size=0.8)+labs(title = "No. of Attacks and Bombings per Year", x= "Year" ,
y="Year Wise Attacks")
ggplotly(qn_plot)
Q3]: Terrorist attacks region wise per year
q3_plot = ggplot(q3,aes(x=iyear,y=Tot_Terr_Att))+
geom_area(aes(fill = region_txt),col='Black')+
facet_wrap(~region_txt, scales="free",ncol=3)+
geom_point(col="black",size=0.8)+
theme(legend.position =' ')+
theme(legend.title = element_blank())+
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Year', title='Terrorist attacks region wise per year',caption ='www.GTD.com')
q3_plot

Q4]: Top 5 type of terror attacks per region
## Selecting by Totall_Attacks
q4_plot = ggplot(q4, aes(x=attacktype1_txt,y=Totall_Attacks))+geom_bar(stat = "Identity",width = .5, aes(fill =attacktype1_txt))+
theme(legend.title = element_blank())+
theme(legend.position = "bottom", legend.key.width = unit(.2, "cm"))+
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Region', title='Top 5 type of terror attacks per region',caption ='www.GTD.com')+
theme_bw() + facet_wrap(~region_txt, scales="free",ncol=3)+
theme(legend.title = element_blank())+
theme(axis.title.x=element_blank(), axis.text.x=element_blank(),axis.ticks.x=element_blank())
q4_plot

## # A tibble: 10 x 2
## targtype1_txt Total_cas
## <chr> <dbl>
## 1 Private Citizens & Property 254205
## 2 Military 146440
## 3 Police 100596
## 4 Business 68728
## 5 Government (General) 61279
## 6 Transportation 53030
## 7 Religious Figures/Institutions 33922
## 8 Terrorists/Non-State Militia 13977
## 9 Educational Institution 12688
## 10 Government (Diplomatic) 11803
q5_plot = ggplot(q5, aes(x=reorder(targtype1_txt,-Total_cas), y=Total_cas))+
geom_bar(stat = "Identity",aes(fill=targtype1_txt),width = 0.9)+
geom_text(aes(label=`Total_cas`),position = position_stack(vjust=1))+
theme(legend.position = "bottom", legend.key.width = unit(2, "cm"))+
theme_bw()+
theme(legend.title = element_blank())+
scale_y_continuous(breaks = seq(10500,255000,50000))+
theme(axis.text.x=element_blank(),axis.ticks.x = element_blank())+
labs(subtitle="Global Terrorism Data", y="Total casualities ", x="Types of Targets", title="[5]:Heaviest heat target types(based on wounded and killed)")
q5_plot

Q6]: Terrorist attack in India and Pakistan in last 45 years
q6_plot =ggplot(q6,aes(x=iyear,y=Total_terr_att))+
geom_bar(stat = 'identity',aes(fill=country_txt,width = .5))+
theme(legend.title = element_blank())+
theme(legend.position = "bottom", legend.key.width = unit(.5, "cm"))+
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Year', title='Terrorist attack in India and Pakistan in last 45 years',caption ='www.GTD.com')
ggplotly(q6_plot)
#Q7]: Terror attack in United States vs Russian Federation/USSR in last 45 years
q7_plot = ggplot(q7,aes(x=iyear,y=Tot_att))+
geom_bar(stat = 'identity',aes(fill=country_txt,width = .5))+
theme_bw() +
labs(subtitle='From 1970 to 2015',y='No Of Attacks', x='Years', title='Terror attack in United States vs Russian Federation/USSR in last 45 years',caption ='www.GTD.com')
ggplotly(q7_plot)
Q8]: Where are there the most casualties?
Grouping by countrywise
q8a_plot = ggplot(q8, aes(x=reorder(country_txt,-Total_casualties), y=Total_casualties))+
geom_bar(stat = "Identity", aes(fill=country_txt),width = .40)+
theme(axis.text.x = element_text(angle = 45),axis.ticks = element_blank())+
theme(legend.position = "bottom",legend.title = element_blank())+
theme(legend.text = element_text(colour = 'black',
size = 8,face='bold'))+
labs(subtitle='From 1970 to 2015',y='No Of Casualities', x='Countries', title='Where are there the most casualties',caption ='www.GTD.com')
ggplotly(q8a_plot)
grouping by Targetwise
q8b_plot = ggplot(q8b, aes(x=reorder(targtype1_txt,-Total_casualtis), y=Total_casualtis))+
geom_bar(stat = "Identity", aes(fill=targtype1_txt))+theme_bw()+
theme(axis.text.x = element_blank(),axis.ticks = element_blank())+
theme(legend.position = "",legend.title = element_blank())+
theme(legend.text = element_text(colour = 'black', size = 8,face = 'bold'))+
labs(title='Where are there the most casualties',subtitle='From 1970 to 2015',y='No Of Casualities', x='Types of Target',caption ='www.GTD.com')+scale_y_continuous(labels = scales::comma)
ggplotly(q8b_plot)
Q9]: How have casualties evolved throughout the years?
q9_plot = ggplot(q9,aes(x=iyear))+
geom_line(aes(y=total_kill),col='red',size=1)+
geom_line(aes(y=total_wound),col='blue',size=1)+
labs(subtitle='Kills=Red Wounded=Blue',y='No Of Casualities', x='Year', title='How have casualties evolved throughout the years',caption ='www.GTD.com')+
theme_bw()+
theme(axis.text.x = element_text())+
theme(legend.position = " ")+
scale_x_continuous(breaks = seq(1970,2015,5))+
scale_y_continuous(breaks = seq(80,44000,5000))+
geom_point(aes(y=q9$total_kill),col='Black',size=1)+
geom_point(aes(y=q9$total_wound),col='black',size=1)+
theme(legend.text = element_text(colour = 'black', size = 8,face = 'bold'))
ggplotly(q9_plot)
Q10]: What are the casualties by weapon type?
q10_plot = ggplot(q10, aes(x=reorder(weaptype1_txt,-Total_casualtes), y=Total_casualtes))+
geom_bar(stat = "Identity", aes(fill=weaptype1_txt))+theme_bw()+
theme(axis.text.x = element_blank(),axis.ticks.x = element_blank())+
labs(subtitle="Global Terrorism Data",
y="Total Number of Casualties", x="Types of Weapons", title="10]:Total casualties by Weapon Type")+
scale_y_continuous(labels = scales::comma)
ggplotly(q10_plot)
Q11]: Are certain nationalities more targeted? If yes, which one?
q11_plot = ggplot(q11, aes(x=reorder(country_txt,-Total_attacks), y=Total_attacks))+
geom_bar(stat = "Identity", aes(fill=country_txt))+theme_bw()+
theme(axis.text.x = element_blank(),axis.ticks.x = element_blank())+
labs(subtitle="Global Terrorism Data",
y="Total Number of Attacks", x="Countries", title="11]:Targeted Nationalities")
ggplotly(q11_plot)
Q12]: Are some countries better at defending themselves against terrorist attacks? If yes, which is the safest country to live
Less casualtywise
q12_plot = ggplot(q12, aes(x=country_txt))+
geom_bar()+theme_light()+
labs(subtitle="Global Terrorism Data", x="Countries",y="Count", title="12]:Safest Countries")+theme(axis.text.x = element_text(angle = 45))
ggplotly(q12_plot)
Success ratewise
q12b_plot = ggplot(q12b, aes(x=reorder(country_txt,ratio), y=ratio))+
geom_segment(aes(x=country_txt,y=0,xend=country_txt,yend = ratio),color="blue")+geom_point()+
theme_bw()+theme(axis.text.x = element_text(angle = 45),axis.ticks.x = element_blank())+
labs(subtitle="Global Terrorism Data",
y="Ratio of defends per attack", x="Countries", title="12]:Safest Countries")
ggplotly(q12b_plot)